{"id":4784,"date":"2026-05-02T16:39:32","date_gmt":"2026-05-02T08:39:32","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=4784"},"modified":"2026-06-26T20:00:40","modified_gmt":"2026-06-26T12:00:40","slug":"clustering-based-outlier-detection-technique-using-pso-knn","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=clustering-based-outlier-detection-technique-using-pso-knn","title":{"rendered":"Clustering-Based Outlier Detection Technique Using PSO-KNN"},"content":{"rendered":"\n
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<\/i> 2023<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 26, Issue 12<\/a><\/p>\n<\/div>\n\n\n\n

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Sushilata D. Mayanglambam1, 2<\/sup><\/i><\/a>, Rajendra Pamula1<\/sup>, and Shi-Jinn Horng3<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad, Jharkhand-826004, INDIA<\/p>\n\n\n\n

2<\/sup>Department of Computer Engineering, Mizoram University, Aizawl, Mizoram-796004, INDIA<\/p>\n\n\n\n

3<\/sup>Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei-106335, TAIWAN<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0September 25, 2022
Accepted:\u00a0December 12, 2022
Publication Date:\u00a0May 2, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Precision and Recall curve of Proposed PSO, PSO+K-means, K-means, LOF, and LDOF evaluated with k=10 for Forest fire dataset.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202312_26(12).0003<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique. Initially, the data clustering of the considered datasets was carried out using PSO to obtain optimized clusters. In the optimization process, we have adopted Davies-Bouldin (DB) index as a fitness function. The optimized clusters were pruned to exclude densely packed inliers data. Thereafter, the KNN method was employed to detect outliers present in the datasets. Our proposed algorithm was tested for outlier detection on eight different datasets and compared its performance with PSO+K-means, K-means, Local Outlier Factor (LOF), and Local Distance-based Outlier Factor (LDOF) methods. Our results show that the outlier detection efficiency of the proposed method outperforms than other four techniques. We believe that our proposed technique simple and efficient in finding the outliers in various types of datasets and it could be a promising tool for outlier detection in data mining.<\/p>\n\n\n\n

Keywords:\u00a0Particle Swarm Optimization; Davies-Bouldin Index; K-Nearest Neighbors; Outlier Detection<\/em><\/p>\n\n\n\n

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